Synthesis of linear antenna array using flower pollination algorithm

Linear antenna array (LAA) design is a classical electromagnetic problem. It has been extensively dealt by number of researchers in the past, and different optimization algorithms have been applied for the synthesis of LAA. This paper presents a relatively new optimization technique, namely flower pollination algorithm (FPA) for the design of LAA for reducing the maximum side lobe level (SLL) and null control. The desired antenna is achieved by controlling only amplitudes or positions of the array elements. FPA is a novel meta-heuristic optimization method based on the process of pollination of flowers. The effectiveness and capability of FPA have been proved by taking difficult instances of antenna array design with single and multiple objectives. It is found that FPA is able to provide SLL reduction and steering the nulls in the undesired interference directions. Numerical results of FPA are also compared with the available results in the literature of state-of-the-art algorithms like genetic algorithm, particle swarm optimization, cuckoo search, tabu search, biogeography based optimization (BBO) and others which also proves the better performance of the proposed method. Moreover, FPA is more consistent in giving optimum results as compared to BBO method reported recently in the literature.

     

彩色图像颜色量化问题的求解方法

针对K-means聚类算法在彩色图像颜色量化问题中对初始条件依赖性较强而易陷入局部最优的缺点,以及传统智能优化算法在寻优时只考虑了种群层内个体的相互竞争而忽略种群层间相互协作的问题,提出了一种基于K-means的金字塔结构演化策略（PES）彩色图像量化算法。首先,将K-means聚类算法中的聚类损失函数作为新算法的适应度函数;其次,运用PES对色彩进行种群初始化、分层、探索、加速以及聚类等操作;最后,利用新算法对4幅标准彩色测试图像进行不同色彩量化级的量化。实验结果表明,所提算法能够改善K-means聚类算法以及传统智能算法的上述缺陷,在类内均方误差评判准则下,图像的平均失真率比基于PES的算法低12.25%,比差分进化算法低15.52%,比粒子群优化（PSO）算法低58.33%,比K-means算法低15.06%,且随着色彩量化级的减少,算法量化后的图像失真率比其他算法降低更多,此外,算法量化图像的视觉效果优于其他算法。     

一种基于粒子群算法的色彩量化方案

为提高色彩量化算法的效率,基于求解实优化问题时粒子群算法优于遗传算法这一事实,在基于遗传算法的色彩量化算法的基础上,设计了相应的适应度函数,给出了一种基于粒子群算法的色彩量化方案,并通过量化实例对算法的性能进行了比较。实验结果显示,基于粒子群优化算法的色彩量化方法在收敛速度方面明显优于基于遗传算法的色彩量化方法。     

Application of flower pollination algorithm in load frequency control of multi-area interconnected power system with nonlinearity

This work presents an application of bio-inspired flower pollination algorithm (FPA) for tuning proportional–integral–derivative (PID) controller in load frequency control (LFC) of multi-area interconnected power system. The investigated power system comprises of three equal thermal power systems with appropriate PID controller. The controller gain [proportional gain (K _p), integral gain (K _i) and derivative gain (K _d)] values are tuned by using the FPA algorithm with one percent step load perturbation in area 1 (1 % SLP). The integral square error (ISE) is considered the objective function for the FPA. The supremacy performance of proposed algorithm for optimized PID controller is proved by comparing the results with genetic algorithm (GA) and particle swarm optimization (PSO)-based PID controller under the same investigated power system. In addition, the controller robustness is studied by considering appropriate generate rate constraint with nonlinearity in all areas. The result cumulative performance comparisons established that FPA-PID controller exhibit better performance compared to performances of GA-PID and PSO-PID controller-based power system with and without nonlinearity effect.

     

色彩量化的模糊粒子群优化技术

把粒子群算法应用到色彩量化中,结合已有的模糊C均值聚类量化方法,提出了一种基于粒子群优化的色彩量化算法。模糊C均值聚类量化算法是一种局部搜索算法,对初始值较为敏感,容易陷入局部极小值而不能得到全局最优解;PSO算法是一种基于群体的具有全局寻优能力的优化方法。将模糊C均值聚类量化算法和PSO算法结合起来,把模糊C均值聚类量化算法的聚类准则函数作为PSO算法中的粒子适应度函数。仿真实验表明,新算法在均方根误差和峰值信噪比评判准则下能够得到最优的量化结果。     

真实图形的颜色量化

颜色量是真实图形显示的一个重要环节。它的主要任务是将一幅有Ｎ个象素点的真实图用不超过Ｋ（Ｋ《Ｎ）种颜色近似表示，以使这幅图能在颜色表长为Ｋ的显示设备上输出。本文提出了一种基于编码的颜色量化算法，引算法速度快，所生成的图形效果较好。     

基于邻域灰度值聚类的图像色彩量化

在将图像中的多种颜色或灰度量化成数目较少的颜色或灰度的过程中,存在着计算过于复杂、量化后图像偏差较大等问题,鉴于此,提出基于邻域灰度值聚类的图像色彩量化方法.首先结合邻域像素的灰度和空间信息对像素进行一维灰度化;然后采用基于像素灰度加权系数的改进模糊 均值聚类算法对像素进行聚类.分析和实验表明,该方法可以减少量化计算的复杂度,保持图像的整体层次,量化后图像偏差较小,对图像处理具有一定的实用价值.     

基于聚类分析的色彩量化新算法及其应用

针对针织提花,植绒、印染以及金属表面花纹处理等电脑设计中的要求,研究图像重新量化成仅有几种颜色的色彩量化问题,提出一种基于聚类分析的色彩量化新算法,量化图像较好地兼顾了原图像的总体风貌和设计者希望保留的一些特征,该算法计算量小,容易在微电脑中实现,已成功地应用于电脑提花圆机花型ＣＡＤ系统。该算法对一般的色彩量化具有重要意义。     

A novel bat flower pollination algorithm for synthesis of linear antenna arrays

In this paper, a novel algorithm, namely bat flower pollination (BFP) is proposed for synthesis of unequally spaced linear antenna array (LAA). The new method is a combination of bat algorithm (BA) and flower pollination algorithm (FPA). In BFP, both BA and FPA interact with each other to escape from local minima. The results of BFP for solving a set of 13 benchmark functions demonstrate its superior performance as compared to variety of well-known algorithms available in the literature. The novel proposed method is also used for the synthesis of unequally spaced LAA for single and multi-objective design. Simulation results show that BFP is able to provide better synthesis results than wide range of popular techniques like genetic algorithm, differential evolution, cuckoo search, particle swarm optimization, back scattering algorithm and others.

     

基于颜色量化和密度峰聚类的彩色图像分割

彩色图像分割是簇绒地毯数字化制造的关键技术,图像的分割质量直接影响到后续的图像处理。为解决地毯的彩色图像分割问题,针对人眼在RGB颜色空间中感知不均匀的特性,提出了一种基于颜色量化和密度峰聚类的彩色图像分割算法。基于Lab颜色空间进行颜色量化,在HVC颜色空间中用NBS距离来衡量人眼对颜色差异的感知程度,采用改进的密度峰聚类算法自动确定聚类中心,从而分割地毯图案。实验结果表明,该算法能在不影响人眼感知的前提下得到颜色种类少且边缘清晰的地毯分割图像。     

The Earth Mover's Distance as a Metric for Image Retrieval

We investigate the properties of a metric between two distributions, the Earth Mover's Distance (EMD), for content-based image retrieval. The EMD is based on the minimal cost that must be paid to transform one distribution into the other, in a precise sense, and was first proposed for certain vision problems by Peleg, Werman, and Rom. For image retrieval, we combine this idea with a representation scheme for distributions that is based on vector quantization. This combination leads to an image comparison framework that often accounts for perceptual similarity better than other previously proposed methods. The EMD is based on a solution to the transportation problem from linear optimization, for which efficient algorithms are available, and also allows naturally for partial matching. It is more robust than histogram matching techniques, in that it can operate on variable-length representations of the distributions that avoid quantization and other binning problems typical of histograms. When used to compare distributions with the same overall mass, the EMD is a true metric. In this paper we focus on applications to color and texture, and we compare the retrieval performance of the EMD with that of other distances.     

A Hybrid Color Image Quantization Algorithm Based on k-Means and Harmony Search Algorithms

Color quantization is one of the most important preprocessing stages in many applications in computer graphics and image processing. In this article, a new algorithm for color image quantization based on the harmony search (HS) algorithm is proposed. The proposed algorithm utilizes the clustering method, which is one of the most extensively applied methods to the color quantization problem. Two variants of the algorithm are examined. The first is based on a standalone HS algorithm, and the second is a hybrid algorithm of k-means (KM) and HS. The objective of the hybrid algorithm is to strengthen the local search process and balance the quantization quality and computational complexity. In the first stage, the high-resolution color space is initially condensed to a lower-dimensional color space by multilevel thresholding. In the second stage, the compressed colors are clustered to a palette using the hybrid KMHS to obtain final quantization results. The algorithm aims to design a postclustering quantization scheme at the color-space level instead of the pixel level. This significantly reduces the computational complexity while maintaining the quantization quality. Experimental results on some of the most commonly used test images in the quantization literature demonstrate that the proposed method is a powerful method, suggesting a higher degree of precision and robustness compared to existing algorithms.     

An adjustable algorithm for color quantization

Color quantization is an important technique in digital image processing. Generally it involves two steps. The first step is to choose a proper color palette. The second step is to reconstruct an image by replacing original colors with the most similar palette colors. However a problem exists while choosing palette colors. That is how to choose the colors with different illumination intensities (we call them color layers) as well as the colors that present the essential details of the image. This is an important and difficult problem. In this paper, we propose a novel algorithm for color quantization, which considers both color layers and essential details by assigning weights for pixel numbers and color distances. Also this algorithm can tune the quantization results by choosing proper weights. The experiments show that our algorithm is effective for adjusting quantization results and it also has very good quality of quantization.     

CoQuWeiP--使用加权积的色彩量化算法

提出一种新颖的色彩量化算法——CoQuWeiP．该算法通过设立像素个数和色彩距离的权重，综合考虑了色彩层次感和关键细节的要求，并可以调节权重以满足不同色彩量化任务对色彩层次感和关键细节的不同要求．实验表明，文中算法在调节量化结果方面是有效的，量化性能较好．     

Dynamic color quantization of video sequences

We present an efficient algorithm for dynamic adaptive color quantization of 24 bit image (video) sequences, important in multimedia applications. Besides producing hi fidelity 8 bit imagery, our algorithm runs with minimal computational cost and the generated colormaps are robust to small differences in consecutive images. Apart from the two standard color quantization tasks, colormap design and quantizer mapping, our algorithm includes colormap filling-an operation unique to dynamic color quantization. This task solves the problem of screen flicker, a serious problem in dynamic quantization of image sequences, resulting from rapid changes in display of colormaps. Our solution is based on two ideas: including in the current colormap a small set of color representatives from the previous image; assigning representatives to the colormap entries in an order that reduces the difference between contents of equal entries in consecutive colormaps. Our algorithm runs in near real time on medium range workstations     

基于矢量量化和区域生长的彩色图像分割新算法

针对光照变化和阴影对图像分割的不利影响问题,提出了一种基于矢量量化和区域生长的彩色图像分割新算法。该算法不仅考虑了彩色图像的颜色信息,而且也考虑了彩色图像的空间信息。该算法首先利用一种修改的GLA算法对彩色图像进行量化,并根据彩色图像量化的结果选取种子像素;然后基于矢量角相似性准则,并结合像素空间邻接信息,对每一个种子像素进行区域生长;最后利用模糊C-M eans算法来对未能归类的剩余像素进行分类。实验表明,该算法不仅可以在很大程度上克服光照变化及阴影对图像分割的不利影响,而且分割结果与人的主观视觉感知具有良好的一致性。     

一种基于视觉感知的色彩量化算法

提出一种色彩量化算法.该算法将图像中的区域细分为边缘、内部平滑区域和内部纹理区域3个部分,并根据它们对视觉感知的重要程度赋予不同的量化权重,以达到强化视觉上相对重要的边缘和内部平滑区域、弱化视觉上相对不重要的复杂纹理区域的目的.另外,为了在量化效果和时间性能上取得折衷,对HSV色彩空间固定V值的蜂窝状分割量化算法进行改进,实现一种可在整个色彩空间完成动态分割的量化算法.在保证时间性能比原有算法略有改善的前提下,减少色彩量化的误差.实验结果表明,本文算法只需要为数较少的量化色彩就能达到较好的量化效果,特别适用于基于内容的图像检索等应用场合.     

一种利用花粉算法优化的遥感图像分割方法

为提高遥感图像分割的准确性与抗噪性,以学生t分布混合模型为基础,结合K-means与花粉算法的特点,将K-means算法局部寻优能力强以及花粉算法全局寻优能力强的优点相结合,提出一种基于K-means的学生t分布混合模型,用于遥感图像分割.该方法中,根据学生t分布与高斯分布以及柯西分布比较接近的特点,对花粉算法的执行过...     

Image Dequantization: Restoration of Quantized Colors

Color quantization replaces the color of each pixel with the closest representative color, and thus it makes the resulting image partitioned into uniformly-colored regions. As a consequence, continuous, detailed variations of color over the corresponding regions in the original image are lost through color quantization. In this paper, we present a novel blind scheme for restoring such variations from a color-quantized input image without a priori knowledge of the quantization method. Our scheme identifies which pairs of uniformly-colored regions in the input image should have continuous variations of color in the resulting image. Then, such regions are seamlessly stitched through optimization while preserving the closest representative colors. The user can optionally indicate which regions should be separated or stitched by scribbling constraint brushes across the regions. We demonstrate the effectiveness of our approach through diverse examples, such as photographs, cartoons, and artistic illustrations.     

应用多索引加法量化编码的近邻检索算法

目的 海量图像检索技术是计算机视觉领域研究热点之一,一个基本的思路是对数据库中所有图像提取特征,然后定义特征相似性度量,进行近邻检索。海量图像检索技术,关键的是设计满足存储需求和效率的近邻检索算法。为了提高图像视觉特征的近似表示精度和降低图像视觉特征的存储空间需求,提出了一种多索引加法量化方法。方法 由于线性搜索算法复杂度高,而且为了满足检索的实时性,需把图像描述符存储在内存中,不能满足大规模检索系统的需求。基于非线性检索的优越性,本文对非穷尽搜索的多索引结构和量化编码进行了探索新研究。利用多索引结构将原始数据空间划分成多个子空间,把每个子空间数据项分配到不同的倒排列表中,然后使用压缩编码的加法量化方法编码倒排列表中的残差数据项,进一步减少对原始空间的量化损失。在近邻检索时采用非穷尽搜索的策略,只在少数倒排列表中检索近邻项,可以大大减少检索时间成本,而且检索过程中不用存储原始数据,只需存储数据集中每个数据项在加法量化码书中的码字索引,大大减少内存消耗。结果 为了验证算法的有效性,在3个数据集SIFT、GIST、MNIST上进行测试,召回率相比近几年算法提升4%~15%,平均查准率提高12%左右,检索时间与最快的算法持平。结论 本文提出的多索引加法量化编码算法,有效改善了图像视觉特征的近似表示精度和存储空间需求,并提升了在大规模数据集的检索准确率和召回率。本文算法主要针对特征进行近邻检索,适用于海量图像以及其他多媒体数据的近邻检索。